National pathways to the Sustainable Development Goals (SDGs): A comparative review of scenario modelling tools

The recently-adopted global Sustainable Development Goals (SDGs) will have significant implications for national development planning in both developed and developing countries in the post-2015 period to 2030. Integrated, nationally-owned SDG strategies will be at the centre of national efforts to implement the new sustainable development agenda. The long-run processes and systems perspective that are inherent in the SDGs present complex analytical problems for policymakers and analysts. Scenario analysis and quantitative modelling will be important analytical tools to support national sustainable development planning, and there is an increasingly sophisticated suite of models available to decision makers. This paper reviews and assesses a broad range of different quantitative models that have the potential to support national development planning for the SDGs. The study develops a typology and inventory of 80 different models, and then reviews the comparative strengths, weaknesses and general utility of different models through an initial screening and subsequent multi-criteria analysis of short-listed models. Current gaps in model capabilities are highlighted in the context of providing analytical support for national development planning for the SDGs. While some existing models are particularly relevant, it is unlikely that an ideal model can analyse all SDG targets and variables of interest within a single modelling framework. Top-down ‘macro framework’ models are likely to be more useful for undertaking system-level or economy-wide scenario analysis driven by the national long-term goals and targets, and for exploring trade-offs and synergies among sectors. Bottom-up sectoral models will be able to support far more detailed option-level impact analysis of concrete interventions, technologies and investments. Combining both approaches within an analytical framework will provide a robust approach for analysis and decision-making. The results highlight a range of potential gaps in current modelling capabilities, and provide some new tools to assist with model selection.